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LDA Is More Effective than PCA for Dimensionality Reduction in Classification Datasets
Linear discriminant analysis (LDA) for dimensionality reduction while maximizing class separability
Dimensionality reduction can be achieved using various techniques. Eleven such techniques have already been discussed in my popular article, 11 Dimensionality reduction techniques you should know in 2021.
There, you will properly learn the meanings behind some technical terms such as dimensionality and dimensionality reduction.
In short, dimensionality refers to the number of features (variables) in the dataset. The process of reducing the features in the dataset is called dimensionality reduction.
Linear discriminant analysis
Linear discriminant analysis (hereafter, LDA) is a popular linear dimensionality reduction technique that can find a linear combination of input features in a lower dimensional space while maximizing class separability.
Class separability simply means that we keep classes as far as possible while maintaining minimum separation between the data points within each class.
